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import gradio as gr
import os
import sys
from pathlib import Path
import random
import string
import time
from queue import Queue
from threading import Thread
import emoji
def pipe(val):
return val
ru2en = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-ru-en")
text_gen=gr.Interface.load("spaces/phenomenon1981/MagicPrompt-Stable-Diffusion")
def get_prompts(prompt_text):
return text_gen("photo, " + prompt_text + ", high details, harmony, ideal proportions")
proc1=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-2.0")
def restart_script_periodically():
while True:
time.sleep(600) # 10 minutes
try:
os.execl(sys.executable, sys.executable, *sys.argv)
except:
pass
restart_thread = Thread(target=restart_script_periodically, daemon=True)
restart_thread.start()
queue = Queue()
queue_threshold = 800
def add_random_noise(prompt, noise_level=0.00):
if noise_level == 0:
noise_level = 0.00
# Get the percentage of characters to add as noise
percentage_noise = noise_level * 5
# Get the number of characters to add as noise
num_noise_chars = int(len(prompt) * (percentage_noise/100))
# Get the indices of the characters to add noise to
noise_indices = random.sample(range(len(prompt)), num_noise_chars)
# Add noise to the selected characters
prompt_list = list(prompt)
# Add numbers, special characters, and all emojis to the list of characters used to add noise
noise_chars = string.ascii_letters + string.punctuation + ' ' + string.digits + emoji.emojize(":all:")
for index in noise_indices:
prompt_list[index] = random.choice(noise_chars)
return "".join(prompt_list)
def send_it(inputs, noise_level, proc1=proc1):
prompt_with_noise = add_random_noise(inputs, noise_level)
output = proc1(prompt_with_noise)
return output
with gr.Blocks(analytics_enabled=False, css='style.css') as demo:
with gr.Column(elem_id="col-container"):
with gr.Row(elem_id="shorts"):
input_text_ru = gr.Textbox(
label="Short Ru",
placeholder="девушка",
)
Translate = gr.Button("Ru ▶ En", elem_id="translate").style(full_width=False)
input_text = gr.Textbox(
label="Short En",
placeholder="girl",
)
with gr.Row(elem_id="longs"):
with gr.Column(scale=1000):
prompt = gr.Textbox(
label="Prompt",
placeholder="proto, girl, high details",
)
with gr.Column(scale=1, elem_id="longs-fillers"):
Literally = gr.Button("◀ Literally")
Explain = gr.Button("◀ Explain")
with gr.Row(elem_id="params"):
with gr.Column():
noise_level = gr.Slider(minimum=0.0, value=1.0, maximum=3, step=0.1, label="Noise Level")
with gr.Row(elem_id="paints"):
with gr.Column():
run1 = gr.Button("🔻 Paint")
output1=gr.Image()
with gr.Column():
run2 = gr.Button("🔻 Paint")
output2=gr.Image()
with gr.Column():
run3 = gr.Button("🔻 Paint")
output3=gr.Image()
# with gr.Column():
# run4 = gr.Button("🔻 Paint")
# output4=gr.Image()
gr.Markdown("Honor artists: Yayoi Kusama, Pablo Picasso, Leonardo da Vinci, Banksy, Rembrandt, Frida Kahlo, Vincent van Gogh, Henri Matisse, Salvador Dali, Claude Monet, Andy Warhol, Georgia O'Keeffe, Jackson Pollock, Marcel Duchamp, Edward Hopper, Willem de Kooning, Mark Rothko, David Hockney")
gr.Markdown("Popuar Styles: Anime, Abstract, Minimalist, Cyberpunk, Steampunk, Organic, Geometric, Sci-Fi, Futuristic, Vaporwave, Gothic")
gr.Markdown("Known Moods: joyful, light-hearted, exciting, calming, soothing, playful, fun, bright, colourful, dynamic, energetic, passionate, romantic, vibrant, vivid")
gr.Markdown("Typical techniques: oil on canvas, chinese painting, graffiti, watercolour, graphite, cinematic, film noir, fluorescent, moody lighting, silhouette, ultraviolet, x-ray, olaroid, double exposure, fisheye lens, bokeh")
Literally.click(pipe, inputs=[input_text], outputs=[prompt], queue=False)
Translate.click(ru2en, inputs=[input_text_ru], outputs=[input_text], queue=False)
Explain.click(text_gen, inputs=[input_text], outputs=[prompt], queue=False)
run1.click(send_it, inputs=[prompt, noise_level], outputs=[output1])
run2.click(send_it, inputs=[prompt, noise_level], outputs=[output2])
run3.click(send_it, inputs=[prompt, noise_level], outputs=[output3])
# run4.click(send_it, inputs=[prompt, noise_level], outputs=[output4])
demo.launch(enable_queue=True, inline=True, show_api=False)
block.queue(concurrency_count=2)